5 動作環境

sessionInfo()
## R version 4.2.2 (2022-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 22621)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Japanese_Japan.utf8  LC_CTYPE=Japanese_Japan.utf8   
## [3] LC_MONETARY=Japanese_Japan.utf8 LC_NUMERIC=C                   
## [5] LC_TIME=Japanese_Japan.utf8    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] knitr_1.41         ggsci_2.9          ppcor_1.1          MASS_7.3-58.1     
##  [5] GGally_2.1.2       see_0.7.4          report_0.5.5       parameters_0.20.0 
##  [9] performance_0.10.1 modelbased_0.8.5   insight_0.18.8     effectsize_0.8.2  
## [13] datawizard_0.6.5   correlation_0.8.3  bayestestR_0.13.0  easystats_0.6.0   
## [17] patchwork_1.1.2    ggdag_0.2.7        dagitty_0.3-1      forcats_0.5.2     
## [21] stringr_1.5.0      dplyr_1.0.10       purrr_1.0.0        readr_2.1.3       
## [25] tidyr_1.2.1        tibble_3.1.8       ggplot2_3.4.0      tidyverse_1.3.2   
## 
## loaded via a namespace (and not attached):
##  [1] googledrive_2.0.0   colorspace_2.0-3    ellipsis_0.3.2     
##  [4] estimability_1.4.1  fs_1.5.2            rstudioapi_0.14    
##  [7] farver_2.1.1        graphlayouts_0.8.4  ggrepel_0.9.2      
## [10] fansi_1.0.3         mvtnorm_1.1-3       lubridate_1.9.0    
## [13] xml2_1.3.3          codetools_0.2-18    cachem_1.0.6       
## [16] polyclip_1.10-4     jsonlite_1.8.4      broom_1.0.2        
## [19] dbplyr_2.2.1        ggforce_0.4.1       compiler_4.2.2     
## [22] httr_1.4.4          emmeans_1.8.3       backports_1.4.1    
## [25] assertthat_0.2.1    fastmap_1.1.0       gargle_1.2.1       
## [28] cli_3.6.0           tweenr_2.0.2        htmltools_0.5.4    
## [31] tools_4.2.2         igraph_1.3.5        coda_0.19-4        
## [34] gtable_0.3.1        glue_1.6.2          V8_4.2.2           
## [37] Rcpp_1.0.9          cellranger_1.1.0    jquerylib_0.1.4    
## [40] vctrs_0.5.1         ggraph_2.1.0        xfun_0.36          
## [43] rvest_1.0.3         timechange_0.1.1    lifecycle_1.0.3    
## [46] googlesheets4_1.0.1 scales_1.2.1        tidygraph_1.2.2    
## [49] hms_1.1.2           RColorBrewer_1.1-3  yaml_2.3.6         
## [52] curl_4.3.3          gridExtra_2.3       sass_0.4.4         
## [55] reshape_0.8.9       stringi_1.7.8       highr_0.10         
## [58] boot_1.3-28         rlang_1.0.6         pkgconfig_2.0.3    
## [61] evaluate_0.19       lattice_0.20-45     labeling_0.4.2     
## [64] tidyselect_1.2.0    plyr_1.8.8          magrittr_2.0.3     
## [67] bookdown_0.31       R6_2.5.1            generics_0.1.3     
## [70] DBI_1.1.3           pillar_1.8.1        haven_2.5.1        
## [73] withr_2.5.0         modelr_0.1.10       crayon_1.5.2       
## [76] utf8_1.2.2          tzdb_0.3.0          rmarkdown_2.19     
## [79] viridis_0.6.2       grid_4.2.2          readxl_1.4.1       
## [82] reprex_2.0.2        digest_0.6.31       xtable_1.8-4       
## [85] munsell_0.5.0       viridisLite_0.4.1   bslib_0.4.2
Barrett M (2018) Ggdag: Analyze and create elegant directed acyclic graphs. R package version 01 0
Chang W (2018) R graphics cookbook: Practical recipes for visualizing data. “O’Reilly Media, Inc.”
Pearl J, Glymour M an, Jewell NP (2016) Causal inference in statistics: A primer. John Wiley & Sons
Pearl J, Mackenzie D (2018) The book of why: The new scuence of cause and effect. Penguin
Textor J, Zander B van der, Gilthorpe MS, et al (2016) Robust causal inference using directed acyclic graphs: The R package ’dagitty’. Int J Epidemiol 45:1887–1894
Wickham H, Grolemund G (2016) R for data science: Import, tidy, transform, visualize, and model data. “O’Reilly Media, Inc.”
宮川雅巳 (2004) 統計的因果推論―回帰分析の新しい枠組み―. 朝倉書店
岩波データサイエンス刊行委員会 (ed) (2016) 岩波データサイエンス vol. 3 因果推論ー実世界のデータから因果を読む. 岩波書店
星野崇宏 (2009) 調査観察データの統計科学:因果推論・選択バイアス・データ融. 岩波出版
松村優哉, 湯谷啓明, 紀ノ定保礼, 前田和 (2021) RユーザのためのRstudio[実践]入門 tidyverseによるモダンな分析フローの世界 改訂2版. 技術評論社
高橋将宜 (2022) 統計的因果推論の理論と実装. 共立出版